Tuesday, November 1, 2011

Is Siri future of BI?

Siri, a voice-activated "personal assistant" on the new iPhone 4S helps you send messages, set reminders, and search for information. You speak to Siri to ask it questions and give it commands, such as small tasks that you'd like it to complete. For example, ask Siri about the weather, and it will respond out loud with a short summary of the day's weather report and on-screen with a snapshot of the five-day forecast.

What will happen if we integrate Siri with BI applications? If we integrate Siri with BI application then conversation between BI end user and Siri will go like this

Siri: What can I help you with?
CEO: What is our sales revenue?
Siri: It's 300 mn till date. There is growth of 10% Y-o-Y.
CEO: Which is our worst performing region?
Siri: North. Sales revenue has declined 20% compared to the same period last year.
CEO: Text scott why is sales revenue declining? Can we meet tomorrow? (Scott - Regional Manager - North)
CEO: Set up sales review meeting with Scott at 9 am tomorrow.
Siri: Ok. I have set up a meeting at 9am.

Siri makes it so easy to consume the information and hence it is a ideal match for BI applications. Currently Apple doesn't allow third party developers to access Siri. When they open access to Siri, it will definitely change the way people use BI applications. Siri has potential to revolutionize BI application world.

Sunday, September 4, 2011

How to structure business analytics team

Today many organisations are trying to find answer to the following questions

1. How do they structure business analytics team?
2. Who should they hire?
3. Should they have business analytics team as part of IT or BI group?
4. Should they have centralised business analytics team or decentralised team?

Determining business analytics team structure is a very important step for successful roll out of business analytics initiative in enterprise.

I would recommend the following org. structure for business analytics team.

Business analytics team should be decentralised and part of business team. This means that you need to have marketing analytics team as part of your sales & marketing business unit & risk analytics team as part of risk business unit. Business analytics team shouldn't be part of IT or BI group.That's because developing, testing and maintaining complex analytical data models involves significant domain-specific business knowledge. This will also help organisations to embed analytics into their business processes. It is very essential that there is a formal communication channel in place between IT/BI groups and business analytics team. This channel can be used to communicate best practices across various groups.

Business analytics team should comprises of business analysts and statisticians. Business analysts are MBAs with specific business function knowledge. They will be responsible for defining business problem and communicating results of analytics. Statisticians (PhDs or Masters in statistics) will be responsible for building and testing analytical models. I would recommend ratio of 2:1 between business analysts and statisticians.

Data preparation is very important step in building business analytics models. This responsibility should be given to either IT or BI team. If this activity is not performed well or if there is no formal communication channel in place between IT and business analytics teams then your business analytics initiative will fail. This is the most trickiest part of the entire structure. You have to have top management commitment & involvement to make this happen. I had seen rift between business analytics and IT teams in many organisations in past. Business analytics team used to complain that IT team is not giving them data in right format at right time. IT team used to complain that business analytics team is asking for data elements which are not captured in source systems. The only way to solve this problem is to have senior management sponsorship and involvement.

- Posted using BlogPress from my iPad

Sunday, July 17, 2011

How to build an Analytical culture?

Today, many organizations in India are facing challenges to build and sustain an analytical culture within the organization. They face the following challenges 
  1. Lack of top management buy in
  2. Data Quality issues
  3. Lack of right set of people to implement analytics initiatives
  4. Analytics initiatives are being implemented with "Project" mentality
  5. Lack of awareness within business groups 
Many top executives in India think that they are not yet ready for Analytics from data maturity and investment point of view. One issue in gaining the acceptance by executives is the notion that the Analytics intiatives take long time to implement as it requires data warehouse to be built. Many organizations in India are not successful in building a data warehouse. To gain acceptance within executive community, you need to decouple analytics initiatives from data warehouse initiative. You need to tie analytics with strategic issues and identify areas wherein you can show "quick" wins. In order to show quick wins, you need not to pick your entire universe of customers. You can select small segment of your entire customer base or a region for analysis where
  • Data maturity level is high 
  • There is facts driven decisioning culture in place
  • Business impact of analytical results is very high
One of the banks in India, chose HNI(High Networth Individuals) segment for analytics pilot. They managed to show big business impact of using analytics in this small segment of customers and got buy in from their management for investment in analytics initiatives. 

Many organizations in India believe that they need to have perfect data in order to implement analytics. Let me tell you that data will never be perfect. So start right away with what can be done now with the data you have & get top management buy in.Once you have buy in from top management & you demonstrate the value of analytics, companies will invest more in resources and infrastructure to address data issues, paving the way for enterprise analytics deployments.

I always wonder when people tell me that they do not find right people in market for analytics. India has got the largest pool of math and stat people in the world. There are enough number of people available in the market. You need to find them and train them on relavent tools and technologies. If you do not want to invest initially then hire a partner organization to do initial pilot to secure buy in from management. Once management is convinced then start hiring people from the market. I strongly believe that organization can sustain analytical culture only when they build capability internally.

There are many early adoptors of analytics initiative in India.Today, they are finding it difficult to sustain analytical culture in the organization. They started of with implementing analytics with the "Project" mentality. They started with fixed scope and timelines. Once the project got implemented, project team got dismantled. This is a recipe for failure eventhough you have started on this journey before your competition.You need to treat Analytics as ongoing program and keep on enhancing & updating analytical models as your business changes to derive value out of your investments. This requires setting up of business analytics CoE. I shall discuss in my future blog about what it takes to build business analytics CoE.

Lastly, it is very important to create awareness about analytics within business community.You have to be able to clearly communicate the value proposition of analytics and what it means to the business. You have to be able to sell ideas.

You will be failed to build analytical culture if any of the above things go wrong. Organizations which were successful to build analytical culture stop looking at analytics as a tool or a product, but as a component of the business process.

Sunday, April 10, 2011

Simple Yet Powerful Analytics

Last month we did a PoC(Proof Of Concept) for one of the manufacturing organizations in India. During PoC, we found out that their sales revenue was declining in southern region whereas sales revenue was increasing at good pace in other regions of the country. The common sense says that the sales team was not very effective in Southern region. However, when we did analysis, we found out that there were more number of stock outs at dealers  in southern region compared to other regions. This is because of under forecasting of the products done for the southern region. If they would have done correct forecasting of products for south region then they would have gain market share in that region. We were surprised to learn from them that they do manual forecasting of products for a region based on gut feeling of regional sales team.

It is very critical for organizations to do accurate hierarchical sales forecasting at multiple levels(Products  & Geography). Over forecasting will result in excess inventory and higher inventory carrying cost whereas under forecasting will result lost sales opportunity. Today, many organizations in India are doing manual sales forecasting. Manual forecasts quite often results into either excess inventory or stock outs.  In case of excess inventory, you end up offering higher discounts to your dealers or customers. This will directly impact your margins and profitability. In case of stock outs, you lose out sales opportunities.

Sales forecasting is not as simple as it sounds. It requires usage of high end statistical forecasting solutions. If you do not have it yet then start looking for such solutions. It will help you add competitive advantage over your competition.     

Tuesday, March 29, 2011

Reporting Vs Analytics

I had met up with yet another prospect last week who was not aware of difference between reporting and analytical solutions. I would blame reporting tool vendor for this who has started confusing customers by positioning reporting tools as analytical solution. There are several differences between reporting and analytical solutions.

1. Business Objective
Reporting: Reporting solutions will help you measure performance of various business entities relative to business plan or target. It will help you convert data into information

Analytics: Analytical solutions will help you identify new products, customer segments, reduce cost, risk & fraud. It will help you convert information into knowledge.

Example: Reporting solution will tell you number of stock outs by items by store whereas Analytical solution will tell you about optimum amount of quantity that you need to keep in your store to minimize stock outs and opportunity cost.

2. Information output
Reporting: Reporting solution output will help you quantify past performance.

Analytics: Analytical solution output will help you infer unknown facts and relationships. It will also help you quantify future probabilities.

Example: Reporting solution will tell you about best selling products in your portfolio whereas analytical solution will tell you about probability of  buying a particular product when your customer visits your store next time.

3.  Output
 Reporting: Historical standard reports, dashboards, KPIs, cubes for OLAP.
Analytics: Predictive models, scores, forecasts.

Reporting: Top 10 products by revenue, Top 10 customers by region
Analytics: Cross Sell/Up Sell Model, Forecasting by Product by Region by Time

4. Queries
Reporting: Known, simple queries which can be easily optimized.

Analytics: Queries that become very complex as they evolve via iteration.

Reporting solution will help you answer the following questions
1. What happened? When did it happen?
2. How many? How often? Where?
3. Where exactly is the problem? How do I find the answers?

Analytical solution will help you answer the following questions
1. Why is it happening? What opportunities am I missing?
2. What if these trends continue? How much is needed? When will it be needed?
3. What will happen next? How will it affect my business?
4. How do we do things better? What is the best decision for a complex problem?

Thomas Davenport has rightly said "Organizations that fail to invest in the proper analytic technologies will be unable to compete in a fact-based business environment."

Davenport says organizations successfully competing on analytics exhibit a set of common attributes, including:
  • CEO commitment – To use analytics as a basis for competition requires commitment from the top of the organization. It requires an allocation of resources, long-term funding and, in some cases, a shift in culture. 
  • Strategic focus – Successful users of analytics don't just use analytics in general. They first define their distinctive capability and then use analytics to support that capability. 
  • Enterprise application – Firms that compete on analytics don't manage it locally. They eliminate fiefdoms of data, centralize the data and expertise, and manage analytics at the enterprise level.

Saturday, February 12, 2011

Tablet PCs & BI: Are they made for each other?

Six months ago mobile Business Intelligence discussions focused on BI access via smartphones like the iPhone and the Blackberry. Gartner analyst Kurt Schlegel included mobile BI as one of nine emerging trends in the business intelligence software market , but he and others (including me) were focused on smartphones. They’re ideal devices to provide alerts, displays of a few key performance indicators and PDFs of pre-fab reports for senior executives and road warriors. However, the small screen size and tiny keys preclude use of analytics, drill down investigations or other standard BI exercises.

Right now end user demand for smartphone-based BI access tools is weak due to those and other limitations. Tablets offer important capabilities to road warriors due to the bigger screen, touch keyboard and other features. The ability for two people to look at the same data on the same device and start poking the touch screen to drill down for detailed insights will enhance collaboration in restaurant meetings and other out-of-the-office settings.

A production manager and an inventory manager standing in a warehouse and analyzing real-time analytics forecasts to determine optimum storage and shipping plans will be one of many compelling applications driving tablet use for BI access.

Users have already started getting value out of these devices. They have changed the way they work. This is a fundamental paradigm shift. Tablet devices like iPad can turn BI from “Get me the information” to “I will get the information myself (as long as I can do this on my iPad).” The more people in an organization that are getting their own information, the more the use of information for decision-making will evolve in that organization, as users become more accustomed to the data they have and how they can use it. Answering one question with interactive data often leads to users seeing another question they want to ask, and hence navigating through the information -- providing the navigation is easy enough to do. This is in contrast to the type of BI landscape where other people provide management with a printed report or even a static online report.

Table PCs like iPad will change the way user consumes the information. Apple iPad makes it so easy to consume information that many users, who were reluctant to use BI, will start using BI. As part of its Predicts 2011 body of research, Gartner has identified four key BI predictions to help organizations plan for 2011 and beyond. They have predicted that 33% of of BI functionality will be consumed via handheld devices by 2013. These are very conservative numbers. These numbers will increase exponentially as more and more tablet devices are released in the market. It will eventually bring down price of tablets and make it more affordable.

In a recent Aberdeen survey of 277 companies with business intelligence systems, employee usage of these systems doubled with mobile BI. Tablet BI will be more interactive and enables us to access information when and where decisions are made, not just when we are at our desks. Tablet PC’s large screen provides a superior user experience to that of smartphone and lets mobile users drill deeper into data. Increase in usage of tablets will increase BI usage and vice versa. They are made for each other.